Can weather data add a pinch of salt to your prediction models?

In the stormy June of 1944, D-Day – the date of the Normandy Landings was looming. The allied forces backed up by tanks and military were painstakingly assembled and prepped in the lead-up to the invasion.

The supreme commander of the Allied Forces had to ensure the success of the D-Day, originally scheduled for June 5. Meteorologist predicted June 5 to be disastrous, weather wise. With all the odds against them, the commander found a window of opportunity – Quiet weather, moderate winds, low tide and a more-or-less full moon – June 6 was a go.

In more recent past, the nasty winter weather throughout large parts of the U.S. in 2014 had been the theme to then economic data. By April, businesses nationwide suffered around $15 billion in weather-related losses. Of course retailers, airlines and logistics firms led the pack.

Weather is the biggest influence on consumer behavior after the state of the economy, according to the British Retail Consortium. Weather conditions influence human behavior, it sets an individual’s emotional tone and the choices they make. The sales performance of just about every consumer good can be impacted by a particular type of weather condition. Weather influences the type of clothes we wear, the type of food we eat, the car we buy/rent and the type of holiday we want to book.

It determines how much we are willing to spend on these goods. ‘The Journal of Retailing and Consumer Services’ shows that on a sunny day consumers were willing to pay an average of $4.61 for a green tea compared with $3.35 in overcast conditions. However, it’s not just sunlight that impacts purchase behavior. Temperature, rainfall, snow and wind will all determine what products are more likely to sell.

Supermarkets, such as TESCO, look at the forecast on a daily basis and alter stock requirements and pricing accordingly. They are able to forecast weather-determined consumer demand with unerring accuracy, knowing that for example, a 1 degree rise in temperature can trigger a 22% increase in demand for fizzy drinks, 60% decrease in porridge, and 90% increase in garden furniture.

Weather can add a pinch of salt to your prediction models. The question is how to improve your prediction models using weather and how to thwart yourself from making some very common cardinal sins while building weather dependent models.

Using right modeling techniques, you can identify variables influencing the customer purchasing behavior for a particular product in a particular region. Along with the weather elements, these behavioral models should include other variables that can affect the sales such as day-of-week, time of year, proximity to holidays, promotions and discounts etc.

Few important things to be kept in mind:

Geographic Location – Weather means different things for people living in different habitats and climates. Therefore you should avoid building models by brute force, which would consider the weather and the sales trends in Minneapolis and Miami with no difference. Your model might end-up estimating lower sales of cold beverages in colder regions than the actual and vice-versa if it does not distinguish between regions.